OpenRaiser/Intern-Atlas huggingface dataset:
Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding
Venue: arXiv cs.RO 2019
Year: 2019

While training an end-to-end navigation network in the real world is usually of high cost, simulation provides a safe and cheap environment in this training stage. However, training neural network models in simulation brings up the problem of how to effectively transfer the model from simulation to the real world (sim-to-real). In this work, we regard the environment representation as a crucial element in this transfer process and propose a visual information pyramid (VIP) model to systematically investigate a practical environment representation. A novel representation composed of spatial and semantic information synthesis is then established accordingly, where noise model embedding is particularly considered. To explore the effectiveness of this representation, we compared the performance with representations popularly used in the literature in both simulated and real-world scenarios. Results suggest that our environment representation stands out. Furthermore, an analysis on the feature map is implemented to investigate the effectiveness through inner reaction, which could be irradiative for future researches on end-to-end navigation.
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
Venue: arXiv cs.NI 2019
Year: 2019

The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To ensure robust communication against jamming, an interference-aware routing protocol is developed that allows nodes to avoid communication holes created by jamming attacks. Then, a distributed cooperation framework, based on deep reinforcement learning, is proposed that allows nodes to assess network conditions and make deep learning-driven, distributed, and real-time decisions on whether to participate in data communications, defend the network against jamming and eavesdropping attacks, or jam other transmissions. The objective is to maximize the network performance that incorporates throughput, energy efficiency, delay, and security metrics. Simulation results show that the proposed jamming-aware routing approach is robust against jamming and when throughput is prioritized, the proposed deep reinforcement learning approach can achieve significant (measured as three-fold) increase in throughput, compared to a benchmark policy with fixed roles assigned to nodes.
HyperProv: Decentralized Resilient Data Provenance at the Edge with Blockchains
Venue: arXiv cs.DC 2019
Year: 2019

Data provenance and lineage are critical for ensuring integrity and reproducibility of information in research and application. This is particularly challenging for distributed scenarios, where data may be originating from decentralized sources without any central control by a single trusted entity. We present HyperProv, a general framework for data provenance based on the permissioned blockchain Hyperledger Fabric (HLF), and to the best of our knowledge, the first system that is ported to ARM based devices such as Raspberry Pi (RPi). HyperProv tracks the metadata, operation history and data lineage through a set of built-in queries using smart contracts, enabling lightweight retrieval of provenance data. HyperProv provides convenient integration through a NodeJS client library, and also includes off-chain storage through the SSH file system. We evaluate HyperProv's performance, throughput, resource consumption, and energy efficiency on x86-64 machines, as well as on RPi devices for IoT use cases at the edge.
Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT
Venue: arXiv cs.CL 2019
Year: 2019

Various deep learning algorithms have been developed to analyze different types of clinical data including clinical text classification and extracting information from 'free text' and so on. However, automate the keyword extraction from the clinical notes is still challenging. The challenges include dealing with noisy clinical notes which contain various abbreviations, possible typos, and unstructured sentences. The objective of this research is to investigate the attention-based deep learning models to classify the de-identified clinical progress notes extracted from a real-world EHR system. The attention-based deep learning models can be used to interpret the models and understand the critical words that drive the correct or incorrect classification of the clinical progress notes. The attention-based models in this research are capable of presenting the human interpretable text classification models. The results show that the fine-tuned BERT with the attention layer can achieve a high classification accuracy of 97.6%, which is higher than the baseline fine-tuned BERT classification model. In this research, we also demonstrate that the attention-based models can identify relevant keywords that are strongly related to the clinical progress note categories.
Google Summer of Code: Student Motivations and Contributions
Venue: arXiv cs.SE 2019
Year: 2019

Several open source software (OSS) projects expect to foster newcomers' onboarding and to receive contributions by participating in engagement programs, like Summers of Code. However, there is little empirical evidence showing why students join such programs. In this paper, we study the well-established Google Summer of Code (GSoC), which is a 3-month OSS engagement program that offers stipends and mentors to students willing to contribute to OSS projects. We combined a survey (students and mentors) and interviews (students) to understand what motivates students to enter GSoC. Our results show that students enter GSoC for an enriching experience, not necessarily to become frequent contributors. Our data suggest that, while the stipends are an important motivator, the students participate for work experience and the ability to attach the name of the supporting organization to their resumés. We also discuss practical implications for students, mentors, OSS projects, and Summer of Code programs.
A multi-label, dual-output deep neural network for automated bug triaging
Venue: arXiv cs.SE 2019
Year: 2019

Bug tracking enables the monitoring and resolution of issues and bugs within organizations. Bug triaging, or assigning bugs to the owner(s) who will resolve them, is a critical component of this process because there are many incorrect assignments that waste developer time and reduce bug resolution throughput. In this work, we explore the use of a novel two-output deep neural network architecture (Dual DNN) for triaging a bug to both an individual team and developer, simultaneously. Dual DNN leverages this simultaneous prediction by exploiting its own guess of the team classes to aid in developer assignment. A multi-label classification approach is used for each of the two outputs to learn from all interim owners, not just the last one who closed the bug. We make use of a heuristic combination of the interim owners (owner-importance-weighted labeling) which is converted into a probability mass function (pmf). We employ a two-stage learning scheme, whereby the team portion of the model is trained first and then held static to train the team--developer and bug--developer relationships. The scheme employed to encode the team--developer relationships is based on an organizational chart (org chart), which renders the model robust to organizational changes as it can adapt to role changes within an organization. There is an observed average lift (with respect to both team and developer assignment) of 13%-points in 11-fold incremental-learning cross-validation (IL-CV) accuracy for Dual DNN utilizing owner-weighted labels compared with the traditional multi-class classification approach. Furthermore, Dual DNN with owner-weighted labels achieves average 11-fold IL-CV accuracies of 76% (team assignment) and 55% (developer assignment), outperforming reference models by 14%- and 25%-points, respectively, on a proprietary dataset with 236,865 entries.
Statistical Topology of Bond Networks with Applications to Silica
Venue: arXiv cs.CG 2019
Year: 2019

Whereas knowledge of a crystalline material's unit cell is fundamental to understanding the material's properties and behavior, there are not obvious analogues to unit cells for disordered materials despite the frequent existence of considerable medium-range order. This article views a material's structure as a collection of local atomic environments that are sampled from some underlying probability distribution of such environments, with the advantage of offering a unified description of both ordered and disordered materials. Crystalline materials can then be regarded as special cases where the underlying probability distribution is highly concentrated around the traditional unit cell. Four descriptors of local atomic environments suitable for disordered bond networks are proposed and applied to molecular dynamics simulations of silica glasses. Each of them reliably distinguishes the structure of glasses produced at different cooling rates, with the $H_1$ barcode and coordination profile providing the best separation.
Interpretable Deep Neural Networks for Facial Expression and Dimensional Emotion Recognition in-the-wild
Venue: arXiv cs.LG 2019
Year: 2019

In this project, we created a database with two types of annotations used in the emotion recognition domain : Action Units and Valence Arousal to try to achieve better results than with only one model. The originality of the approach is also based on the type of architecture used to perform the prediction of the emotions : a categorical Generative Adversarial Network. This kind of dual network can generate images based on the pictures from the new dataset thanks to its generative network and decide if an image is fake or real thanks to its discriminative network as well as help to predict the annotations for Action Units and Valence Arousal due to its categorical nature. GANs were trained on the Action Units model only, then the Valence Arousal model only and then on both the Action Units model and Valence Arousal model in order to test different parameters and understand their influence. The generative and discriminative aspects of the GANs have performed interesting results.
A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning
Venue: arXiv cs.CV 2019
Year: 2019

In the modern content-based image retrieval systems, there is an increasingly interest in constructing a computationally effective model to predict the interestingness of images since the measure of image interestingness could improve the human-centered search satisfaction and the user experience in different applications. In this paper, we propose a unified framework to predict the binary interestingness of images based on discriminant correlation analysis (DCA) and multiple kernel learning (MKL) techniques. More specially, on the one hand, to reduce feature redundancy in describing the interestingness cues of images, the DCA or multi-set discriminant correlation analysis (MDCA) technique is adopted to fuse multiple feature sets of the same type for individual cues by taking into account the class structure among the samples involved to describe the three classical interestingness cues, unusualness,aesthetics as well as general preferences, with three sets of compact and representative features; on the other hand, to make good use of the heterogeneity from the three sets of high-level features for describing the interestingness cues, the SimpleMKL method is employed to enhance the generalization ability of the built model for the task of the binary interestingness classification. Experimental results on the publicly-released interestingness prediction data set have demonstrated the rationality and effectiveness of the proposed framework in the binary prediction of image interestingness where we have conducted several groups of comparative studies across different interestingness feature combinations, different interestingness cues, as well as different feature types for the three interestingness cues.
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization
Venue: arXiv cs.NE 2019
Year: 2019

Population diversity plays a key role in evolutionary algorithms that enables global exploration and avoids premature convergence. This is especially more crucial in dynamic optimization in which diversity can ensure that the population keeps track of the global optimum by adapting to the changing environment. Dynamic constrained optimization problems (DCOPs) have been the target for many researchers in recent years as they comprehend many of the current real-world problems. Regardless of the importance of diversity in dynamic optimization, there is not an extensive study investigating the effects of diversity promotion techniques in DCOPs so far. To address this gap, this paper aims to investigate how the use of different diversity mechanisms may influence the behavior of algorithms in DCOPs. To achieve this goal, we apply and adapt the most common diversity promotion mechanisms for dynamic environments using differential evolution (DE) as our base algorithm. The results show that applying diversity techniques to solve DCOPs in most test cases lead to significant enhancement in the baseline algorithm in terms of modified offline error values.
Open Source and Sustainability: the Role of Universities
Venue: arXiv cs.CY 2019
Year: 2019

One important goal in sustainability is making technologies available to the maximum possible number of individuals, and especially to those living in less developed areas (Goal 9 of SDG). However, the diffusion of technical knowledge is hindered by a number of factors, among which the Intellectual Property Rights (IPR) system plays a primary role. While opinions about the real effect of IPRs in stimulating and disseminating innovation differ, there is a growing number of authors arguing that a different approach may be more effective in promoting global development. The success of the Open Source (OS) model in the field of software has led analysts to speculate whether this paradigm can be extended to other fields. Key to this model are both free access to knowledge and the right to use other people's results. Abstract After reviewing the main features of the OS model, we explore different areas where it can be profitably applied, such as hardware design and production; we finally discuss how academical institutions can (and should) help diffusing the OS philosophy and practice. Widespread use of OS software, fostering of research projects aimed to use and develop OS software and hardware, the use of open education tools, and a strong commitment to open access publishing are some of the discussed examples.
Development of Computational Thinking in High School Students: A Case Study in Chile
Venue: arXiv cs.CY 2019
Year: 2019

Most efforts to incorporate computational thinking in K-12 education have been focused on students in their first cycles of school education and have used visual tools, such as Scratch and Alice. Fewer research projects have studied the development of computational thinking in students in their last years of school, who usually have not had early formal preparation to acquire these skills. This study provides evidence of the effectiveness of teaching programming in C++ (a low-level language) to develop computational thinking in high school students in Chile. By applying a test before and after a voluntary C ++ programming workshop, the results show a significant improvement in computational thinking at the end of the workshop. However, we also observed that there was a tendency to drop out of the workshop among students with lower levels of initial computational thinking. Tenth-grade students obtained lower final scores than eleventh and twelfth-grade students. These results indicate that teaching a low-level programming language is useful, but it has high entry-barriers.
Global Reactions to the Cambridge Analytica Scandal: An Inter-Language Social Media Study
Venue: arXiv cs.IR 2019
Year: 2019

Currently, there is a limited understanding of how data privacy concerns vary across the world. The Cambridge Analytica scandal triggered a wide-ranging discussion on social media about user data collection and use practices. We conducted an inter-language study of this online conversation to compare how people speaking different languages react to data privacy breaches. We collected tweets about the scandal written in Spanish and English between April and July 2018. We used the Meaning Extraction Method in both datasets to identify their main topics. They reveal a similar emphasis on Zuckerberg's hearing in the US Congress and the scandal's impact on political issues. However, our analysis also shows that while English speakers tend to attribute responsibilities to companies, Spanish speakers are more likely to connect them to people. These findings show the potential of inter-language comparisons of social media data to deepen the understanding of cultural differences in data privacy perspectives.
ICPS-net: An End-to-End RGB-based Indoor Camera Positioning System using deep convolutional neural networks
Venue: arXiv cs.CV 2019
Year: 2019

Indoor positioning and navigation inside an area with no GPS-data availability is a challenging problem. There are applications such as augmented reality, autonomous driving, navigation of drones inside tunnels, in which indoor positioning gets crucial. In this paper, a tandem architecture of deep network-based systems, for the first time to our knowledge, is developed to address this problem. This structure is trained on the scene images being obtained through scanning of the desired area segments using photogrammetry. A CNN structure based on EfficientNet is trained as a classifier of the scenes, followed by a MobileNet CNN structure which is trained to perform as a regressor. The proposed system achieves amazingly fine precisions for both Cartesian position and Quaternion information of the camera.
Using Lexical Features for Malicious URL Detection -- A Machine Learning Approach
Venue: arXiv cs.CR 2019
Year: 2019

Malicious websites are responsible for a majority of the cyber-attacks and scams today. Malicious URLs are delivered to unsuspecting users via email, text messages, pop-ups or advertisements. Clicking on or crawling such URLs can result in compromised email accounts, launching of phishing campaigns, download of malware, spyware and ransomware, as well as severe monetary losses. A machine learning based ensemble classification approach is proposed to detect malicious URLs in emails, which can be extended to other methods of delivery of malicious URLs. The approach uses static lexical features extracted from the URL string, with the assumption that these features are notably different for malicious and benign URLs. The use of such static features is safer and faster since it does not involve crawling the URLs or blacklist lookups which tend to introduce a significant amount of latency in producing verdicts. The goal of the classification was to achieve high sensitivity i.e. detect as many malicious URLs as possible. URL strings tend to be very unstructured and noisy. Hence, bagging algorithms were found to be a good fit for the task since they average out multiple learners trained on different parts of the training data, thus reducing variance. The classification model was tested on five different testing sets and produced an average False Negative Rate (FNR) of 0.1%, average accuracy of 92% and average AUC of 0.98. The model is presently being used in the FireEye Advanced URL Detection Engine (used to detect malicious URLs in emails), to generate fast real-time verdicts on URLs. The malicious URL detections from the engine have gone up by 22% since the deployment of the model into the engine workflow. The results obtained show noteworthy evidence that a purely lexical approach can be used to detect malicious URLs.
The Sounds of Music : Science of Musical Scales III -- Indian Classical
Venue: arXiv cs.SD 2019
Year: 2019

In the previous articles of this series, we have discussed the development of musical scales particularly that of the heptatonic scale which forms the basis of Western classical music today. In this last article, we take a look at the basic structure of scales used in Indian classical music and how different `raga's are generated through the simple process of scale shifting.
Cost Sharing over Combinatorial Domains: Complement-Free Cost Functions and Beyond
Venue: arXiv cs.GT 2019
Year: 2019

We study mechanism design for combinatorial cost sharing. Imagine that multiple items or services are available to be shared among a set of interested agents. The outcome of a mechanism in this setting consists of an assignment, determining for each item the set of players who are granted service, together with respective payments. Although there are several works studying specialized versions of such problems, there has been almost no progress for general combinatorial cost sharing domains until recently \cite{DobzinskiO17}. The main goal of our work is to further understand this interplay in terms of budget balance and social cost approximation. Towards this, we provide a refinement of cross-monotonicity (trace-monotonicity) that is applicable to iterative mechanisms. The trace here refers to the order in which players become finalized. On top of this, we also provide two parameterizations of cost functions which capture the behavior of their average cost-shares. Based on our trace-monotonicity property, we design a scheme of ascending cost sharing mechanisms which is applicable to the combinatorial cost sharing setting with symmetric submodular valuations. Using our first cost function parameterization, we identify conditions under which our mechanism is weakly group-strategyproof, $O(1)$-budget-balanced and $O(H_n)$-approximate with respect to the social cost. Finally, we consider general valuation functions and exploit our second parameterization to derive a more fine-grained analysis of the Sequential Mechanism introduced by Moulin. This mechanism is budget balanced by construction, but in general only guarantees a poor social cost approximation of $n$. We identify conditions under which the mechanism achieves improved social cost approximation guarantees.
Algorithm and hardness results on neighborhood total domination in graphs
Venue: arXiv cs.DM 2019
Year: 2019

A set $D\subseteq V$ of a graph $G=(V,E)$ is called a neighborhood total dominating set of $G$ if $D$ is a dominating set and the subgraph of $G$ induced by the open neighborhood of $D$ has no isolated vertex. Given a graph $G$, \textsc{Min-NTDS} is the problem of finding a neighborhood total dominating set of $G$ of minimum cardinality. The decision version of \textsc{Min-NTDS} is known to be \textsf{NP}-complete for bipartite graphs and chordal graphs. In this paper, we extend this \textsf{NP}-completeness result to undirected path graphs, chordal bipartite graphs, and planar graphs. We also present a linear time algorithm for computing a minimum neighborhood total dominating set in proper interval graphs. We show that for a given graph $G=(V,E)$, \textsc{Min-NTDS} cannot be approximated within a factor of $(1-\varepsilon)\log |V|$, unless \textsf{NP$\subseteq$DTIME($|V|^{O(\log \log |V|)}$)} and can be approximated within a factor of $O(\log Δ)$, where $Δ$ is the maximum degree of the graph $G$. Finally, we show that \textsc{Min-NTDS} is \textsf{APX}-complete for graphs of degree at most $3$.